In the medical field, a doctor displays a medical image obtained by imaging a patient on a monitor, interprets the displayed medical image, and observes the state of a lesion and its change with time. The following are available as apparatuses designed to generate this type of medical images:                Computed Radiography (CR)        Computed Tomography (CT)        Magnetic Resonance Imaging (MRI)        Ultrasound System (US)        
With the purpose of reducing the load of such interpretation on a doctor, there has been developed a diagnosis support apparatus which automatically detects a lesion or the like by digitizing a medical image and performing image analysis, and performs computer-aided diagnosis (Kawata, Niki, and Ohmatsu, “Curvature Based Internal Structure Analysis of Pulmonary Nodules Using Thoracic 3-D CT Images”, the transactions of the Institute of Electronics, Information and Communication Engineers D-II, Vol. J83-D-II, No. 1, pp. 209-218, January 2000).
Computer-Aided Diagnosis will be referred to as CAD hereinafter. CAD is designed to automatically detect an abnormal shadow candidate as a lesion. In this abnormal shadow detection processing, performing computer processing of image data representing a radiographic image will detect an abnormal tumor shadow indicating a cancer or the like, a high-density minute calcified shadow, or the like. Presenting this detection result can reduce the load of interpretation on a doctor and improve the accuracy of the interpretation result.
The Japanese Society of CT Screening (NPO) provides determination criteria and follow-up guidelines for lung cancer CT screening by single slice helical CT to help doctors avoid misdiagnosis in interpretation.
A diagnosis support apparatus to perform computer support diagnosis calculates an abnormal shadow candidate always considering the balance between “sensitivity” and “misdiagnosis detection”, which contradict each other (Japanese Patent No. 3417595).
For example, increasing “sensitivity” as a parameter for adjusting the number of tumor shadow candidates to be extracted will increase the number of times of “misdiagnosis detection”, that is, extraction of shadows which are not actually tumors.
As described above, increasing “sensitivity” will reduce oversight but increase “misdiagnosis detection”. A false positive lesion candidate is called an FP (false positive).    Patent reference 1: Japanese Patent No. 3417595    Non-patent reference 1: Kawata, Niki, and Ohmatsu, “Curvature Based Internal Structure Analysis of Pulmonary Nodules Using Thoracic 3-D CT Images”, the transactions of the Institute of Electronics, Information and Communication Engineers D-II, Vol. J83-D-II, No. 1, pp. 209-218, January 2000    Non-patent reference 2: determination criteria and follow-up guidelines for lung cancer CT screening by single slice helical CT, “The Japanese Society of CT Screening (NPO)”